TY  - JOUR
T1  - The Bioprocess World Model for Simulating Cell-State Evolution, Manufacturing Intervention, Process Uncertainty, and Critical Quality Attributes
A1  - Grace Collins
A1  - Oliver Evans
A1  - Noah Williams
A1  - Ethan Brooks
JF  - International Journal of Pharmaceutical Research and Allied Sciences
JO  - Int J Pharm Res Allied Sci
SN  - 2277-3657
Y1  - 2026
VL  - 15
IS  - 2
DO  - 10.51847/xB0RdsqItf
SP  - 30
EP  - 39
N2  - Biopharmaceutical manufacturing increasingly uses mechanistic models, statistical predictors, process analytical technologies, metabolic reconstructions, and digital-twin concepts to anticipate culture behavior and product quality. However, an accurate predictor of a selected process variable is not necessarily a model of the evolving manufacturing system. It may lack a persistent representation of cell state, explicit intervention semantics, multiscale temporal dynamics, calibrated uncertainty, or a traceable connection between process actions and critical quality attributes. This article proposes a bioprocess world-model architecture as an original conceptual synthesis for representing partially observed manufacturing systems and simulating conditional future consequences. The architecture separates measurements from latent biological and process states, represents interventions as explicit inputs to temporal transitions, couples intracellular, cellular, population, extracellular, equipment, and product-quality layers, and propagates multiple forms of uncertainty. Sensor and analytical data are incorporated through controlled state estimation, recalibration, and structurally governed updating rather than unrestricted continual learning. Validation is framed as perturbation-based consequence prediction: alternative interventions should be simulated from comparable initial states and assessed against prospective or held-out biological, process, and quality outcomes. Proposed readiness levels distinguish conceptual specification, verified components, retrospective coherence, prospective prediction, perturbation validity, and bounded decision evaluation. Major failure modes include partial observability, non-identifiability, policy confounding, clonal and analytical drift, omitted-state error, scale dependence, and unsupported extrapolation. The architecture does not establish empirical validity, manufacturing benefit, regulatory acceptance, or universal applicability. Its contribution is a testable framework for organizing models, observations, interventions, uncertainty, quality consequences, and evidence requirements within explicitly declared manufacturing boundaries. 
UR  - https://ijpras.com/article/the-bioprocess-world-model-for-simulating-cell-state-evolution-manufacturing-intervention-process-k5bhhwfriummqf2
ER  -